Pretrained Medical Representations for the Practical Screening of Drug Repositioning Candidates

The paper introduces a unified pre‑training framework for medical representations that incorporates hierarchical sub‑token aggregation, partial masking, and cross‑reference mechanisms to better capture the structure of medical codes. The resulting model outperforms existing BERT‑based approaches on pre‑training tasks and downstream clinical predictions, such as dementia onset and hospitalization. An in‑silico drug repositioning study for Alzheimer’s disease demonstrates the framework’s ability to rediscover known drugs and prioritize new hypotheses without external literature, establishing a workflow for hypothesis generation and prioritization based on observational data.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.